Cognitive J^(2)SAC:Joint Jamming,Sensing,and Communication Under Antagonistic Environment

Integrated sensing and communication(ISAC)is regarded as a recent advanced technology,which is expected to realize the dual functions of sensing and communication simultaneously in one system.Nevertheless,it still faces the challenges of the information security and transmission robustness caused by the openness of wireless channel,especially under antagonistic environment.Hence,this article develops a generalized framework,named cognitive joint jamming,sensing and communication(cognitive J2SAC),to empower the current sensing/communication/jamming system with a“brain”for realizing precise sensing,reliable communication and effective jamming under antagonistic environment.Three kinds of gains can be captured by cognitive J2SAC,including integrated gain,cooperative gain and cognitive gain.Moreover,we highlight the enabling mechanism among jamming,sensing,and communication,as well as illustrating several typical use cases of cognitive J2SAC.Furthermore,several key enabled technologies are analyzed and a typical sensing enhance integrated communication and jamming case study is discussed to verify the effectiveness of the proposed method.Last but not the least,the future directions are listed before concluding this article.Integrated sensing and communication(ISAC)is regarded as a recent advanced technology,which is expected to realize the dual functions of sensing and communication simultaneously in one system.Nevertheless,it still faces the challenges of the information security and transmission robustness caused by the openness of wireless channel,especially under antagonistic environment.Hence,this article develops a generalized framework,named cognitive joint jamming,sensing and communication(cognitive J2SAC),to empower the current sensing/communication/jamming system with a“brain”for realizing precise sensing,reliable communication and effective jamming under antagonistic environment.Three kinds of gains can be captured by cognitive J2SAC,including integrated gain,cooperative gain and cognitive gain.Moreover,we highlight the enabling mechanism among jamming,sensing,and communication,as well as illustrating several typical use cases of cognitive J2SAC.Furthermore,several key enabled technologies are analyzed and a typical sensing enhance integrated communication and jamming case study is discussed to verify the effectiveness of the proposed method.Last but not the least,the future directions are listed before concluding this article.

Network-Connected UAV Communications: Potentials and Challenges

This article explores the use of network-connected unmanned aerial vehicle(UAV) communications as a compelling solution to achieve high-rate information transmission and support ultra-reliable UAV remote command and control. We first discuss the use cases of UAVs and the resulting communication requirements, accompanied with a flexible architecture for network-connected UAV communications. Then, the signal transmission and interference characteristics are theoretically analyzed, and subsequently we highlight the design and optimization considerations, including antenna design, nonorthogonal multiple access communications, as well as network selection and association optimization. Finally, case studies are provided to show the feasibility of network-connected UAV communications.

HF Communications: Past, Present, and Future

High frequency(HF) communication, commonly covering frequency range between 3 and 30 MHz, is an important wireless communication paradigm to offer over-thehorizon or even global communications with ranges up to thousands of kilometers via skywave propagation with ionospheric refraction. It has widespread applications in fields such as emergency communications in disaster areas, remote communications with aircrafts or ships and non-light-of-the-sight military operations. This tutorial article overviews the history of HF communication, demystifies the recent advances, and provides a preview of the next few years, which the authors believe will see fruitful outputs towards wideband, intelligent and integrated HF communications. Specifically, we first present brief preliminaries on the unique features of HF communications to facilitate general readers in the communication community. Then, we provide a historical review to show the technical evolution on the three generations of HF communication systems. Further, we highlight the key challenges and research directions. We hope that this article will stimulate more interests in addressing the technical challenges on the research and development of future HF radio communication systems.

Spectrum Prediction Based on GAN and Deep Transfer Learning:A Cross-Band Data Augmentation Framework

This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.

Cross-Band Spectrum Prediction Based on Deep Transfer Learning

Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.

Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

Edge Semantic Cognitive Intelligence for 6G Networks:Novel Theoretical Models,Enabling Framework,and Typical Applications

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks,but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated.To this end,this article discusses the concepts of edge intelligence from the semantic cognitive perspective.Two instructive theoretical models for edge semantic cognitive intelligence(ESCI)are first established.Afterwards,the ESCI framework orchestrating deep learning with semantic communication is discussed.Two representative applications are present to shed light on the prospect of ESCI in 6G networks.Some open problems are finally listed to elicit the future research directions of ESCI.

Spatial-temporal spectrum hole discovery:a hybrid spectrum sensing and geolocation database framework

A hybrid spectrum sensing and geolocation database framework is proposed to tackle the discovery of spatial-temporal spectrum hole in cognitive radio networks.We first analyze the advantages and disadvantages of spectrum sensing-based and geolocation database-based approaches respectively,which motivate us to further propose a hybrid protocol framework by effectively integrating the benefits of both spectrum sensing and geolocation database.Specifically,in the proposed hybrid approach,the goal is to maximize the utilization of spatialtemporal spectrum hole while satisfying the protection constraints for the primary users.Analytical and numerical results demonstrate the superior performance of the proposed hybrid approach over the existing spectrum sensing only and geolocation database only approaches,in terms of interference-free throughput.This article serves as a fundamental framework for advancing the design of hybrid approaches for spatial-temporal spectrum hole discovery.

Proactive optimization of transmission power and 3D trajectory in UAV-assisted relay systems with mobile ground users

In this paper,an Unmanned Aerial Vehicle(UAV)-assisted relay communication system is studied,where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destination node.Specifically,an average outage probability minimization problem is formulated firstly,with the constraints on the transmission power of the source node,the maximum energy consumption budget,the transmission power,the speed and acceleration of the flying UAV relay.Next,the closed-form of outage probability is derived,under the hybrid line-of-sight and non-line-of-sight probability channel model.To deal with the formulated nonconvex optimization,a long-term proactive optimization mechanism is developed.In particular,firstly,an approximation for line-of-sight probability and a reformulation of the primal problem are given,respectively.Then,the reformulated problem is transformed into two subproblems:one is the transmission power optimization with given UAV’s trajectory and the other is the trajectory optimization with given transmission power allocation.Next,two subproblems are tackled via tailoring primal–dual subgradient method and successive convex approximation,respectively.Furthermore,a proactive optimization algorithm is proposed to jointly optimize the transmission power allocation and the three-dimensional trajectory.Finally,simulation results demonstrate the performance of the proposed algorithm under various parameter configurations.

Topology tracking of dynamic UAV wireless networks

Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network,especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.

An informational perspective of semantic cognition process

Information theory(IT)is the derivation and foundation of information science,and has become one of the most mature,complete and systematic components in information and communication fields within these years.This article extends classic IT from the traditional form aspect to the semantic aspect and gives an informational perspective of semantic cognition process(SCP),which is motivated by the stringent requirements of information predigestion and machine cognition in cognitive wireless networks.To begin with,we establish three key viewpoints on semantic,which are semantic objectivity,semantic conditional uniformity and semantic dependency,as the basis and premise of this article.Next,we establish a comprehensive theoretical framework of SCP in terms of the natural connotation,the three-layer framework and the mathematical model of SCP.Then we give a couple of primary theorems of SCP as well as their practical instructions.Furthermore,the research challenges ahead are presented in this article.